Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning
Authors: Chunwei Ma, Zhanghexuan Ji, Ziyun Huang, Yan Shen, Mingchen Gao, Jinhui Xu
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Putting everything together, i Voro achieves up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, Tiny Image Net, and Image Net-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, i Voro enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications. |
| Researcher Affiliation | Academia | Chunwei Ma1, Zhanghexuan Ji1, Ziyun Huang2, Yan Shen1, Mingchen Gao1, Jinhui Xu1 1Department of Computer Science and Engineering, University at Buffalo 2Computer Science and Software Engineering, Penn State Erie 1{chunweim,zhanghex,yshen22,mgao8,jinhui}@buffalo.edu 2{zxh201}@psu.edu |
| Pseudocode | Yes | Algorithm 1: Voronoi Diagram-based Logistic Regression. Algorithm 2: i Voro Algorithm. Algorithm 3: i Voro-D Algorithm. |
| Open Source Code | Yes | Our code is available at https://machunwei.github.io/ivoro/. |
| Open Datasets | Yes | Three standard datasets, CIFAR-100 (Krizhevsky et al., 2009), Tiny Image Net (Le & Yang, 2015) and Image Net-Subset (Deng et al., 2009a) for CIL are used for method evaluation. |
| Dataset Splits | Yes | We follow the popular benchmarking protocol in exemplar-free CIL used by (Liu et al., 2021b; Zhu et al., 2021; Douillard et al., 2020; Hou et al., 2019) in which the inital phase contains a half of the classes while the subsequent phases each has 1 5, 1 10, or 1 20 of the remaining classes. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., specific GPU/CPU models, memory details). |
| Software Dependencies | No | The paper does not explicitly list software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Specifically, for each phase τ {1, ..., t}, the local dataset Dτ is used to train a logistic regression model (restricted by Thm. 2.1) with weight decay β at 0.0001 and initial learning rate at 0.001. |